A Novel Approach to Robust Blind Classification of Remote Sensing Imagery

In this paper we propose a novel method for the robust classification of blurred and noisy images that incorporates ideas from data fusion. The technique is applicable to blind situations in which the exact blurring function is unknown. The approach treats differently deblurred versions of the same image as distinct correlated sensor readings of the same scene. The images are fused during the classification process to provide a more reliable result. We show analytically that the various restorations can be treated as images acquired from different but correlated sensor readings. Experimental results demonstrate the potential of the method for robust classification of imagery. 1 Introduction Classification is an information processing task in which specific entities are mapped to general categories. For the classification of remote sensing multispectral images, the specific goal is to assign each vector-valued pixel of the multispectral image to its appropriate category using tonal a..